in this article, a new method for fusion of high resolution optical and radar data for higher quality road extraction is presented. the proposed methodology consists of two stages of separate road detection from each data and knowledge based fusion of results. neural networks are separately applied on high resolution ikonos and terrasar-x images for road detection, using a variety of texture pa...

This project aims to research the current technology for recognising road signs in real-time from a moving vehicle. The most promising technology for intelligent vehicle systems is vision sensors and image processing, so this is examined the most thoroughly. Different processing algorithms and research around the world concerned with sign recognition are investigated. A functioning system has a...

Driver support systems of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing...

A driver assistance system (DAS) should support the driver by monitoring road and vehicle events and presenting relevant and timely information to the driver. It is impossible to know what a driver is thinking, but we can monitor the driver’s gaze direction and compare it with the position of information in the driver’s viewfield to make inferences. In this way, not only do we monitor the drive...

Despite a trend towards increased road safety, fatal and/or serious accidents induce a significant toll each year. We work towards improved road safety through the development of an intelligent driver assistance system that monitors the driver’s performance and assists with vehicle guidance. This complex task is decomposed into subsystems including lane-keeping, obstacle avoidance, sign detecti...

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by ...

This paper presents a new algorithm for color detection and segmentation of road signs in poor light conditions. The images were taken by a digital camera mounted in a car. The RGB channels of the digital images were enhanced separately by histogram equalization, and then a color constancy algorithm was applied to extract the true colors of the sign. The resultant image was then converted into ...

This paper deals with object recognition in outdoor environments. In this type of environments, lighting conditions cannot be controlled and predicted, objects can be partially occluded, and their position and orientation is not known a priori. The chosen type of objects is traffic or road signs, due to their usefulness for sign maintenance, inventory in highways and cities, Driver Support Syst...

In Minnesota, 70 percent of all intersection-related fatal crashes for the period of 2006 to 2008 occurred at rural, through/stop intersections [1]. The Minnesota Department of Transportation identified improving the design and operation of intersections as a critical emphasis area in the Minnesota Comprehensive Highway Safety Plan [2]. At these intersections, sight restrictions caused by verti...